The present invention relates generally to radiographic imaging and, more particularly, to a system and method for acquisition and reconstruction of contrast-enhanced and artifact-reduced CT images. The present invention further relates to system and method for processing CT data to increase contrast-to-noise ratio (CNR) and reduce artifacts in the reconstructed image.
Typically, in radiographic systems, an x-ray source emits x-rays toward a subject or object, such as a patient or a piece of luggage. Hereinafter, the terms “subject” and “object” may be interchangeably used to describe anything capable of being imaged. The x-ray beam, after being attenuated by the subject, impinges upon an array of radiation detectors. The intensity of the radiation beam received at the detector array is typically dependent upon the attenuation of the x-rays through the scanned object. Each detector of the detector array produces a separate signal indicative of the attenuated beam received by each detector. The signals are transmitted to a data processing system for analysis, further processing and, ultimately, image reconstruction.
In a similar fashion, radiation detectors are employed in emission imaging systems such as used in nuclear medicine (NM) gamma cameras and Positron Emission Tomography (PET) systems. In these systems, the source of radiation is no longer an x-ray source, rather it is a radiopharmaceutical introduced into the body being examined. In these systems each detector of the array produces a signal in relation to the localized intensity of the radiopharmaceutical concentration in the object. Similar to conventional x-ray imaging, the strength of the emission signal is also attenuated by the interlying body parts. Each detector element of the detector array produces a separate signal indicative of the emitted beam received by each detector element. The signals are transmitted to a data processing system for processing, analysis, and image reconstruction.
In most computed tomography (CT) imaging systems, the x-ray source and the detector array are rotated about a gantry encompassing an imaging volume around the subject. X-ray sources typically include x-ray tubes, which emit the x-rays as a fan or cone beam from the anode focal point. X-ray detector assemblies typically include a collimator for reducing scattered x-ray photons from reaching the detector, a scintillator adjacent to the collimator for converting x-rays to light energy, and a photodiode adjacent to the scintillator for receiving the light energy and producing electrical signals therefrom. Typically, each scintillator of a scintillator array converts x-rays to light energy. Each photodiode detects the light energy and generates a corresponding electrical signal. The outputs of the photodiodes are then transmitted to the data acquisition system and then to the processing system for image reconstruction.
Conventional CT imaging systems utilize detectors that convert x-ray photon energy into current signals that are integrated over a time period, then measured and ultimately digitized. A drawback of such detectors is their inability to provide independent data or feedback as to the energy and incident flux rate of photons detected. That is, conventional CT detectors have a scintillator component and photodiode component wherein the scintillator component illuminates upon reception of x-ray photons and the photodiode detects illumination of the scintillator component and provides an integrated electrical current signal as a function of the intensity and energy of incident x-ray photons. While it is generally recognized that CT imaging would not be a viable diagnostic imaging tool without the advancements achieved with conventional CT detector design, a drawback of these integrating detectors is their inability to provide energy discriminatory data or otherwise count the number and/or measure the energy of photons actually received by a given detector element. Accordingly, recent detector developments have included the design of an energy discriminating detector that can provide photon counting and/or energy discriminating feedback. In this regard, the detector can be caused to operate in an x-ray counting mode, an energy measurement mode of each x-ray event, or both. That is, such energy discriminating detectors are capable of not only x-ray counting but also providing a measurement of the energy level of each x-ray detected.
In x-ray projection systems and CT imaging modalities that do not utilize energy discrimination, the contrast between target objects and background objects is formed by differences in x-ray attenuation between target and background materials. In this case, larger differences in x-ray attenuation translate to improved differentiation of the target materials from the background materials. However, typically, images contain multiple materials and mixtures of materials that may yield similar contrasts in an x-ray projection or reconstructed CT image and make differentiation of the target objects difficult.
In systems utilizing energy integrating detectors, the detector signal is formed as a specific weighted sum of x-ray events. The specific weighting function for an energy integrating detector is proportional to the energy for each x-ray photon. Therefore, the high energy x-rays are weighted more heavily than the low energy x-rays. The nature of the detector dictates the this specific weighting function. As a result, information available in the x-ray data and from the a priori knowledge of anatomy is not typically considered, which may lead to sub-optimal weighting.
In systems utilizing energy discriminating detectors, it is possible to differentiate materials within the subject of the imaging. As such, some methods have been proposed to apply a generalized weight factor which is some specific function of the x-ray energy to improve the detective quantum efficiency (DQE) in specific applications, such as mammography applications. However, such generalized x-ray energy weighting functions include limitations when actually implemented because they are globally implementated for all pixels in an image and do not specifically take into account the energy-dependent, x-ray attenuation properties of each tissue locally. For example, these general x-ray energy weighting functions which may be optimized for enhancing high atomic materials such as bone and iodine contrast agents will result in reduced CNR when imaging soft tissue materials having low atomic numbers. Furthermore, performance degradation is incurred due to the limited number of energy bins used during data acquisition and due to detector electronic noise. Accordingly, when such generalized x-ray energy weighted acquisitions are performed instead of equally weighted acquisitions, increased noise is experienced in the reconstructed image.
Another drawback of generalized x-ray energy weighted approaches is that low energy photons are heavily weighted, which increases noise within a reconstructed image. That is, typically, low energy photons can not penetrate the imaging subject effectively and, thus, the majority of low energy photons are attributable to noise. Accordingly, when a generalized x-ray energy weighted approach is applied and low energy photons are heavily weighted, noise, which comprises a large percentage of the low energy photons, is increased within the reconstructed image.
Additionally, conventional CT imaging can create a visualization of the density of the tissue and substances imaged in the subject. The density is derived as related to x-ray attenuation of the tissue and is encoded as a grey scale value in order to form an image. Density information is often used to segment regions of the images and associate those regions with certain biological tissues. For example, high attenuation is often associated with bone. By performing segmentation based on density information, it is possible to remove bone from the image so as to generate a soft-tissue-only image.
In addition, the technique of dual energy material discrimination uses the value of attenuation acquired at two or more energies to differentiate tissues. This dual energy technique creates two individual images that projects the energy sensitive response of each tissue onto that of two “basis” materials. The result of this technique is a set of two images, each of which is a projection onto a single basis vector. Since the energy dependence of attenuation is related to atomic number, additional atomic-number-related information is displayed which is different and complimentary to the gray-scale density map. This technique of data analysis and dual energy projection image formation differentiates tissues and allows new diagnostic interpretations by physicians or medical specialists. However, it requires the viewing of multiple images during evaluation and diagnosis.
When utilizing systems employing energy discriminating detectors, additional information is available from such energy selective detector systems that can be used to produce information related to the atomic number of tissues without extrapolating the atomic number information from the density of the tissue. However, this additional information is not typically utilized during the display of medical images.
Conventional CT images represent the x-ray attenuation of an object under investigation. The CT number for a given pixel within the image is determined by a linear attenuation coefficient for that pixel averaged over the x-ray spectrum. Beam hardening errors occur because the energy spectrum is different at different locations across the volume of the object. As a result, conventional CT does not provide quantitative image values. Beam-hardening errors are often present in conventional CT images because a given material may be located at different locations that are at varying distances from the x-ray source and, therefore, the given material provides differing contributions to the x-ray projection.
Due to the polychromatic x-ray beam spectrum and energy dependent attenuation coefficients, the x-ray attenuation and the path length is generally non-linear. This non-linear relationship can cause beam-hardening artifacts such as non-uniformity, shading, and streaking. These image artifacts can lead to misdiagnosis and limit the usefulness to perform quantitative analysis on CT images.
Since more than 80% of a human body is water, beam hardening artifacts can be at least partially corrected by remapping the projection data based on the x-ray beam spectrum and water attenuation characteristics. However, when the scanned object is highly heterogeneous and its attenuation characteristics deviate significantly from those of water, the water beam hardening correction become inadequate. For example, in a head scan, where there is a large amount of bony structure present in addition to the soft tissues, water beam hardening correction is an insufficient compensation technique. Accordingly, residual errors and image artifacts are present after water beam hardening correction is applied and dark banding between dense objects and degraded bone-brain interface may be found in the reconstructed image.
To combat these artifacts, some approaches have been proposed. However, these approaches rely on an assumed average bone thickness and density or on iteratively estimated bone contents in the water corrected image. As such, should the actual bone structure deviate even slightly from the assumed or estimated the bone structure, over-correction or under-correction errors may cause less than optimal imaging results.
It would therefore be desirable to design a method and system capable of reconstructing an image with reduced beam hardening artifacts and with increased material differentiation to aid in distinguishing various materials within an image. It would also be desirable to have a system and method capable of reconstructing an image that is substantially free of beam-hardening artifacts and with material variations clearly distinguished or identified.